Scanning electron microscope study comparing four root canal preparation techniques in small curved canals.
Various instrumentation techniques have been proposed and examined with conflicting results. They include hand and ultrasonic techniques and combinations of the two. In the present study we assessed the effectiveness of four preparation methods for cleaning small, curved root canals, using backscattered-imaging scanning electron microscopy (SEM). The methods were: (i) step-back without initial coronal flaring; (ii) step-back with coronal flaring; (iii) step-back with initial coronal flaring and finished by ultrasonic irrigation; and (iv) ultrasonics only. Eighty freshly extracted maxillary and mandibular molars were randomly placed into four treatment groups of 20 teeth each. After preparation, roots were sectioned longitudinally and examined wet by SEM. Each canal was qualitatively evaluated and the groups compared for removal of debris and smear layer, both overall and at each level (apical, middle and coronal). There were no statistically significant differences between the techniques, either overall or within any of the regions. When comparing regions (regardless of technique) the middle level was cleaner than the apical or coronal levels. In conclusion, efficacy differed little among the techniques; none of them completely removed smear layer and all left debris.
Heard
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Department of Endodontics, University of Iowa College of Dentistry, Iowa City 52242, USA.
Walton
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D003786:Dental Pulp Cavity / Q000648:ultrastructure*; D005069:Evaluation Studies as Topic; D006801:Humans; D066298:In Vitro Techniques; D008334:Mandible; D008437:Maxilla; D008855:Microscopy, Electron, Scanning; D008963:Molar; D011897:Random Allocation; D018915:Root Canal Preparation / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D016369:Smear Layer; D018709:Statistics, Nonparametric; D007507:Therapeutic Irrigation / Q000379:methods; D014464:Ultrasonic Therapy / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data